We build AI systems for consequential analytical work.
Your models are static. The world isn't.
Markets move every day. Models don't. Analysts still spend the morning figuring out which changes actually touch the book, opening models one by one, and reconstructing why the last forecast was there. IRIS understands the model before the AI call, makes the analyst's explicit, and preserves how that thinking changes over time. Frontier AI can then focus on what matters, where it matters, and why.
When the world changes, which of my models should change with it - and why?
Every model holds investment knowledge. IRIS makes that knowledge usable across the book.
Connect the forecasts, Methods, evidence, and history inside your models. Ask one economic question across the portfolio, then follow the answer into the reasoning behind each company's view.
A move in credit spreads may matter to funding cost in one company, reinvestment yield in another, and expected losses somewhere else.
IRIS keeps those economic relationships with each model, making them durable and queryable across the book while preserving how each company responds differently.
Work done in one name becomes usable knowledge when the same driver matters elsewhere. The PM can compare assumptions, explore a shared economic scenario, and find the evidence behind a view before opening individual workbooks.
Understanding compounds across the investment process.
That same connected view makes the morning review possible. Each night, IRIS refreshes market and economic data, identifies which models are affected, and reruns the analysis that depends on what changed.
By morning, PM Search can ask across the refreshed book: what changed, which models moved, which views held, and where attention belongs. Each answer leads back to the forecast, Method, evidence, and prior work behind it.
The PM sees where to look. The analyst sees why.
The PM View points to a name. The analyst opens the model with its structure already understood. The Research Rail sits beside the spreadsheet with the model’s analytical context already established. Inspect the Method, evidence, prior work, and what a changed view would affect — then ask IRIS a question, save a thought, or tell IRIS what changed without leaving the model.
IRIS can preview a proposed change before the analyst applies it to the model.
Before frontier AI reasons about the workbook, IRIS establishes what can be known exactly: periods, formulas, dependencies, forecast structure, responsibilities, historical relationships, and the relevant inputs and outputs.
At import, IRIS preserves what it understood about the model as a durable baseline. The model can change later without rewriting what IRIS knew then.
The differentiated forecast comes from how the analyst weighs evidence, frames the drivers, and decides when the view should change. IRIS makes that judgment explicit as a Method — readable, editable, reviewable, and executable through the workbook. IRIS writes Methods. Analysts own them.
A generic frontier model can reason. But hand it a workbook cold and it first has to figure out the periods, formulas, forecast structure, and which parts of the file actually matter.
Some of its intelligence is spent rediscovering facts that software can establish exactly before it reaches the investment question.
IRIS does the model archaeology once. It prepares the problem before the model call, so the reasoning can be about funding cost, unit growth, margins, credit losses, pricing, volume, or operating leverage - whatever actually drives the forecast. We call this Semantic Compression. In tests it has led to a 20x reduction in token use.
The point is not a shorter prompt. It is a better-defined question and more of the model's intelligence spent on the economics.
Let computers establish the facts. Let frontier models reason about the economics.
Why is the model different now? A changed estimate should not erase the reason the prior estimate existed — or what IRIS understood about the model before it changed.
Model Memory is how that thinking evolves.
IRIS preserves that import-time understanding, then keeps subsequent Methods, Versions, Runs, Results, and rationale attached to the analytical changes that produced them.
Follow Versions from left to right. Open the Run beneath a change. Compare its Result with what came before and recover the reasoning that produced it. The lineage replaces overwritten cells and scattered notes with a record an analyst can actually revisit.
Excel stays the model.
Frontier AI provides the intelligence.
The analyst owns the judgment.
IRIS is the harness that keeps them connected.
Less model archaeology. Faster detection of what matters. Fewer models opened unnecessarily. A durable record of why the view changed.